geo-infer-spm
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
Best use case
geo-infer-spm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
Teams using geo-infer-spm should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-SPM/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-spm Compares
| Feature / Agent | geo-infer-spm | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-SPM
## Instructions
### Core Capabilities
- **GLM fitting**: General linear models with spatial design matrices
- **Random field theory**: Multiple comparison correction for spatial data
- **Cluster inference**: Cluster-level and peak-level statistics
- **Contrast testing**: T-contrasts and F-contrasts on spatial maps
- **Visualization**: Interactive time series explorer (mean±SD + residuals)
### Key Imports
```python
from geo_infer_spm.core.glm import GLMModel
from geo_infer_spm.core.random_field import RandomFieldTheory
from geo_infer_spm.models.data_models import SPMData, SPMResult
from geo_infer_spm.visualization.interactive import create_time_series_explorer
```
## Examples
```python
from geo_infer_spm.models.data_models import SPMData
import numpy as np
data = SPMData(
data=np.random.randn(100, 50),
coordinates=np.column_stack([
np.random.uniform(-90, 90, 100), # latitudes
np.random.uniform(-180, 180, 100) # longitudes
])
)
```
## Guidelines
- Coordinates must be valid: latitude ∈ [-90, 90], longitude ∈ [-180, 180]
- GLM implementation is Alpha status — spatial design matrices in progress
- Time series explorer uses Plotly for interactive mean±SD visualization
- Test: `uv run python -m pytest GEO-INFER-SPM/tests/ -v`
### Integrations
- **MATH** → Spatial statistics and topology input
- **BAYES** → Bayesian GLM parameter estimation
- **SPACE** → Spatial residual fields from H3 grids
- **AI** → Feature engineering for statistical maps